Abstract: A learning device performs learning by an autoencoder, using waveform data with changes over time that is obtained from an intrinsic movement of an object. The learning device performs persistent homology conversion to calculate a change in the number of connected component in accordance with a change of a threshold for a value of the first waveform data. The learning device determines abnormality based on a determination result of a learner to which output data of the autoencoder obtained from the waveform data and output data obtained from the persistent homology conversion are input, and in which machine learning about abnormality of the waveform data has been performed.

Abstract: A learning device performs learning by an autoencoder, using waveform data with changes over time that is obtained from an intrinsic movement of an object. The learning device performs persistent homology conversion to calculate a change in the number of connected component according to a threshold change in a value direction for the waveform data. The learning device determines abnormality based on a determination result of a learner to which output data of the autoencoder obtained from the waveform data and output data obtained from the persistent homology conversion are input, and in which machine learning about abnormality of the waveform data has been performed.